Kazuhiro Miki
Papers
1
Total Citations
8
H-Index
1
About
Kazuhiro Miki’s research centers on robust speech recognition in real-world acoustic environments, with a particular focus on environmental sound source identification. His most cited work, “Environmental sound source identification based on hidden Markov model for robust speech recognition” (2003), addresses a fundamental challenge in hands-free communication: how machines can mimic the human ability to isolate target speech from surrounding noise. By applying hidden Markov models to identify and distinguish environmental sounds, Miki contributed to making speech recognition systems more resilient in dynamic, noisy settings—a critical step for applications in smart devices, hearing aids, and human-robot interaction. Though his citation count (8) is modest, his work represents an early, targeted effort to bridge the gap between human auditory perception and machine listening, laying groundwork for later advances in acoustic scene analysis. Miki’s research underscores the importance of context-aware audio processing, and his approach continues to inform studies on robust speech interfaces. His contributions are particularly valuable for students and researchers exploring the intersection of signal processing, machine learning, and human-computer interaction in challenging acoustic conditions.
Research Focus
Key Achievements
Top Papers
- 1